Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums
This provides an easy-to-deploy surveillance solution for retail environments, though it is incremental as it builds on existing robot vacuum platforms.
The paper tackles visual surveillance by mounting a smartphone on commercial robot vacuums to capture images and estimate poses, achieving 0.83 m relative pose error for localization and 0.97 m positional error for object mapping of over 100 items.
This paper presents Piggyback Camera, an easy-to-deploy system for visual surveillance using commercial robot vacuums. Rather than requiring access to internal robot systems, our approach mounts a smartphone equipped with a camera and Inertial Measurement Unit (IMU) on the robot, making it applicable to any commercial robot without hardware modifications. The system estimates robot poses through neural inertial navigation and efficiently captures images at regular spatial intervals throughout the cleaning task. We develop a novel test-time data augmentation method called Rotation-Augmented Ensemble (RAE) to mitigate domain gaps in neural inertial navigation. A loop closure method that exploits robot cleaning patterns further refines these estimated poses. We demonstrate the system with an object mapping application that analyzes captured images to geo-localize objects in the environment. Experimental evaluation in retail environments shows that our approach achieves 0.83 m relative pose error for robot localization and 0.97 m positional error for object mapping of over 100 items.